import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DetrImageProcessor





class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def __init__(       self     :     str     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Any=7     ,  snake_case__     :     Optional[Any]=3     ,  snake_case__     :     Any=3_0     ,  snake_case__     :     Optional[Any]=4_0_0     ,  snake_case__     :     Union[str, Any]=True     ,  snake_case__     :     int=None     ,  snake_case__     :     Union[str, Any]=True     ,  snake_case__     :     Tuple=1 / 2_5_5     ,  snake_case__     :     str=True     ,  snake_case__     :     Tuple=[0.5, 0.5, 0.5]     ,  snake_case__     :     Optional[int]=[0.5, 0.5, 0.5]     ,  snake_case__     :     Tuple=True     ,  )      ->	int:
				# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
				_lowerCamelCase				   =  size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  min_resolution
				_lowerCamelCase				   =  max_resolution
				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean
				_lowerCamelCase				   =  image_std
				_lowerCamelCase				   =  do_pad
		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_pad": self.do_pad,
				}



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     int=False		)      ->	List[str]:
				if not batched:
						_lowerCamelCase				   =  image_inputs[0]
						if isinstance(snake_case__     ,  Image.Image		):
								_lowerCamelCase   ,							_lowerCamelCase				   =  image.size
						else:
								_lowerCamelCase   ,							_lowerCamelCase				   =  image.shape[1], image.shape[2]
						if w < h:
								_lowerCamelCase				   =  int(self.size['shortest_edge'] * h / w		)
								_lowerCamelCase				   =  self.size['shortest_edge']
						elif w > h:
								_lowerCamelCase				   =  self.size['shortest_edge']
								_lowerCamelCase				   =  int(self.size['shortest_edge'] * w / h		)
						else:
								_lowerCamelCase				   =  self.size['shortest_edge']
								_lowerCamelCase				   =  self.size['shortest_edge']

				else:
						_lowerCamelCase				   =  []
						for image in image_inputs:
								_lowerCamelCase   ,							_lowerCamelCase				   =  self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
						_lowerCamelCase				   =  max(snake_case__     ,  key=lambda snake_case__		: item[0]		)[0]
						_lowerCamelCase				   =  max(snake_case__     ,  key=lambda snake_case__		: item[1]		)[1]

				return expected_height, expected_width





@require_torch
@require_vision
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   DetrImageProcessor if is_vision_available() else None
		def   _snake_case       (       self     :     Dict		)      ->	List[Any]:
				_lowerCamelCase				   =  DetrImageProcessingTester(self		)
		@property
		def   _snake_case       (       self     :     Dict		)      ->	Dict:
				return self.image_processor_tester.prepare_image_processor_dict()
		def   _snake_case       (       self     :     Dict		)      ->	List[str]:
				_lowerCamelCase				   =  self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(snake_case__     ,  'image_mean'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'image_std'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'do_normalize'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'do_rescale'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'rescale_factor'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'do_resize'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'size'		)		)
				self.assertTrue(hasattr(snake_case__     ,  'do_pad'		)		)
		def   _snake_case       (       self     :     List[str]		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size     ,  {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}		)
				self.assertEqual(image_processor.do_pad     ,  snake_case__		)

				_lowerCamelCase				   =  self.image_processing_class.from_dict(
				    self.image_processor_dict     ,  size=4_2     ,  max_size=8_4     ,  pad_and_return_pixel_mask=snake_case__		)
				self.assertEqual(image_processor.size     ,  {'shortest_edge': 4_2, 'longest_edge': 8_4}		)
				self.assertEqual(image_processor.do_pad     ,  snake_case__		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				pass
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Union[str, Any]:
				# Initialize image_processing
				_lowerCamelCase				   =  self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				_lowerCamelCase				   =  prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=snake_case__		)
				for image in image_inputs:
						self.assertIsInstance(snake_case__     ,  Image.Image		)

				# Test not batched input
				_lowerCamelCase				   =  image_processing(image_inputs[0]     ,  return_tensors='pt'		).pixel_values

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__		)

				self.assertEqual(
				    encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

				# Test batched
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__     ,  batched=snake_case__		)

				_lowerCamelCase				   =  image_processing(snake_case__     ,  return_tensors='pt'		).pixel_values
				self.assertEqual(
				    encoded_images.shape     ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )     ,  )
		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				# Initialize image_processing
				_lowerCamelCase				   =  self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				_lowerCamelCase				   =  prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=snake_case__     ,  numpify=snake_case__		)
				for image in image_inputs:
						self.assertIsInstance(snake_case__     ,  np.ndarray		)

				# Test not batched input
				_lowerCamelCase				   =  image_processing(image_inputs[0]     ,  return_tensors='pt'		).pixel_values

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__		)

				self.assertEqual(
				    encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

				# Test batched
				_lowerCamelCase				   =  image_processing(snake_case__     ,  return_tensors='pt'		).pixel_values

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__     ,  batched=snake_case__		)

				self.assertEqual(
				    encoded_images.shape     ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )     ,  )
		def   _snake_case       (       self     :     Dict		)      ->	Union[str, Any]:
				# Initialize image_processing
				_lowerCamelCase				   =  self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				_lowerCamelCase				   =  prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=snake_case__     ,  torchify=snake_case__		)
				for image in image_inputs:
						self.assertIsInstance(snake_case__     ,  torch.Tensor		)

				# Test not batched input
				_lowerCamelCase				   =  image_processing(image_inputs[0]     ,  return_tensors='pt'		).pixel_values

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__		)

				self.assertEqual(
				    encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

				# Test batched
				_lowerCamelCase				   =  image_processing(snake_case__     ,  return_tensors='pt'		).pixel_values

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.image_processor_tester.get_expected_values(snake_case__     ,  batched=snake_case__		)

				self.assertEqual(
				    encoded_images.shape     ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )     ,  )
		@slow
		def   _snake_case       (       self     :     List[Any]		)      ->	Dict:
				# prepare image and target
				_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,  'r'		) as f:
						_lowerCamelCase				   =  json.loads(f.read()		)

				_lowerCamelCase				   =  {'image_id': 3_9_7_6_9, 'annotations': target}

				# encode them
				_lowerCamelCase				   =  DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'		)
				_lowerCamelCase				   =  image_processing(images=snake_case__     ,  annotations=snake_case__     ,  return_tensors='pt'		)

				# verify pixel values
				_lowerCamelCase				   =  torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
				self.assertEqual(encoding['pixel_values'].shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([0.2796, 0.3138, 0.3481]		)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,  snake_case__     ,  atol=1e-4		)		)

				# verify area
				_lowerCamelCase				   =  torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,  snake_case__		)		)
				# verify boxes
				_lowerCamelCase				   =  torch.Size([6, 4]		)
				self.assertEqual(encoding['labels'][0]['boxes'].shape     ,  snake_case__		)
				_lowerCamelCase				   =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,  snake_case__     ,  atol=1e-3		)		)
				# verify image_id
				_lowerCamelCase				   =  torch.tensor([3_9_7_6_9]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,  snake_case__		)		)
				# verify is_crowd
				_lowerCamelCase				   =  torch.tensor([0, 0, 0, 0, 0, 0]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,  snake_case__		)		)
				# verify class_labels
				_lowerCamelCase				   =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,  snake_case__		)		)
				# verify orig_size
				_lowerCamelCase				   =  torch.tensor([4_8_0, 6_4_0]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,  snake_case__		)		)
				# verify size
				_lowerCamelCase				   =  torch.tensor([8_0_0, 1_0_6_6]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,  snake_case__		)		)



		@slow
		def   _snake_case       (       self     :     Any		)      ->	int:
				# prepare image, target and masks_path
				_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,  'r'		) as f:
						_lowerCamelCase				   =  json.loads(f.read()		)

				_lowerCamelCase				   =  {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

				_lowerCamelCase				   =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

				# encode them
				_lowerCamelCase				   =  DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'		)
				_lowerCamelCase				   =  image_processing(images=snake_case__     ,  annotations=snake_case__     ,  masks_path=snake_case__     ,  return_tensors='pt'		)

				# verify pixel values
				_lowerCamelCase				   =  torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
				self.assertEqual(encoding['pixel_values'].shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([0.2796, 0.3138, 0.3481]		)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,  snake_case__     ,  atol=1e-4		)		)

				# verify area
				_lowerCamelCase				   =  torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,  snake_case__		)		)
				# verify boxes
				_lowerCamelCase				   =  torch.Size([6, 4]		)
				self.assertEqual(encoding['labels'][0]['boxes'].shape     ,  snake_case__		)
				_lowerCamelCase				   =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,  snake_case__     ,  atol=1e-3		)		)
				# verify image_id
				_lowerCamelCase				   =  torch.tensor([3_9_7_6_9]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,  snake_case__		)		)
				# verify is_crowd
				_lowerCamelCase				   =  torch.tensor([0, 0, 0, 0, 0, 0]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,  snake_case__		)		)
				# verify class_labels
				_lowerCamelCase				   =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,  snake_case__		)		)
				# verify masks
				_lowerCamelCase				   =  8_2_2_8_7_3
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,  snake_case__		)
				# verify orig_size
				_lowerCamelCase				   =  torch.tensor([4_8_0, 6_4_0]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,  snake_case__		)		)
				# verify size
				_lowerCamelCase				   =  torch.tensor([8_0_0, 1_0_6_6]		)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,  snake_case__		)		)
def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')